title: Latent subtypes of depression in a community sample of older adults: can depression clusters predict future depression trajectories?
abstract: Identifying sources of heterogeneity in late life depression remains an important focus of psychiatric investigation. Community samples are particularly informative since many older adults have clinically significant depressive symptoms but fail to meet criteria for major depression and older adults generally do not seek treatment for their depressive symptoms. The primary data used for these analyses were those collected in a community-based survey of over 3000 adults age 65 or older followed for up to ten years. Depressive symptoms were measured by the Center for Epidemiologic Studies-Depression scale (CES-D). Latent class analysis was used to identify clusters of participants based on their symptom profiles at baseline. Mixed models were used to examine trajectories of CES-D scores based on cluster assignment. A model with three unique clusters best fit the data. Cluster 1 (59%) had a low probability of any symptom endorsement. Cluster 2 (31%) endorsed as a group some negative affect and somatic symptoms but their endorsement of low positive affect did not differ from Cluster 1. Participants in Cluster 3 (10%) had a higher probability of endorsement of all symptoms compared to Clusters 1 and 2. The results did not appreciably differ when symptom severity was included. Cluster assignment was a significant predictor of change in CES-D score over the ten-year follow-up period, and the effects over time differed by sex. Depressive symptom profiles predict the longitudinal course of depression in a community sample of older adults, findings that are important especially in primary care settings.
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, NC 27710, USA. celia.hybels@duke.edu
authors.forename: Celia F
authors.initials: CF
authors.lastname: Hybels
authors.forename: Lawrence R
authors.initials: LR
authors.lastname: Landerman
authors.forename: Dan G
authors.initials: DG
authors.lastname: Blazer
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D016000:Cluster Analysis; D003863:Depression / Q000145:classification* / Q000175:diagnosis / Q000453:epidemiology* / Q000523:psychology; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D011237:Predictive Value of Tests; D011569:Psychiatric Status Rating Scales; D012111:Residence Characteristics / Q000706:statistics & numerical data; D012944:Social Support
keywords: Depression; Latent class analysis; Symptoms; Trajectories